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FakeNewsNet 假新闻研究数据收集,假新闻、虚假信息、数据挖掘

FakeNewsNet 假新闻研究数据收集,假新闻、虚假信息、数据挖掘

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NLP,News,Social Science,Social Networks Classification

This is a repository for an ongoing data collection project for fake news research at ASU. We describe and compare FakeN......

数据结构 ? 72.61M

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    This is a repository for an ongoing data collection project for fake news research at ASU. We describe and compare FakeNewsNet with other existing datasets in Fake News Detection on Social Media: A Data Mining Perspective. We also perform a detail analysis of FakeNewsNet dataset, and build a fake news detection model on this dataset in Exploiting Tri-Relationship for Fake News Detection

    JSON version of this dataset is available in github here.
    The new version of this dataset described in FakeNewNet will be published soon or you can email authors for more info.

    News Content

    It includes all the fake news articles, with the news content attributes as follows:

    1. source: It indicates the author or publisher of the news article

    2. headline: It refers to the short text that aims to catch the attention of readers and relates well to the major of the news topic.

    3. body_text: It elaborates the details of news story. Usually there is a major claim which shaped the angle of the publisher and is specifically highlighted and elaborated upon.

    4. image_video: It is an important part of body content of news article, which provides visual cues to frame the story.

    Social Context

    It includes the social engagements of fake news articles from Twitter. We extract profiles, posts and social network information for all relevant users.

    1. user_profile: It includes a set of profile fields that describe the users' basic information

    2. user_content: It collects the users' recent posts on Twitter

    3. user_followers: It includes the follower list of the relevant users

    4. user_followees: It includes list of users that are followed by relevant users

    References

    If you use this dataset, please cite the following papers:

    @article{shu2017fake,  title={Fake News Detection on Social Media: A Data Mining Perspective},  author={Shu, Kai and Sliva, Amy and Wang, Suhang and Tang, Jiliang and Liu, Huan},  journal={ACM SIGKDD Explorations Newsletter},  volume={19},  number={1},  pages={22--36},  year={2017},  publisher={ACM} }

    @article{shu2017exploiting,  title={Exploiting Tri-Relationship for Fake News Detection},  author={Shu, Kai and Wang, Suhang and Liu, Huan},  journal={arXiv preprint arXiv:1712.07709},  year={2017} }

    @article{shu2018fakenewsnet,  title={FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media},  author={Shu, Kai and  Mahudeswaran, Deepak and Wang, Suhang and Lee, Dongwon and Liu, Huan},  journal={arXiv preprint arXiv:1809.01286},  year={2018} }


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